The invention relates to a monitoring system and a method for detecting at least one object in an environment by the monitoring system.
A surveillance system is commonly installed in places such as homes, offices and other public areas to monitor activities occurring in these environments. In the surveillance system, cameras, video cameras or other sensing devices are often used to capture images or videos of the environments, and usually an operator (a person) analyzes the captured images or videos to understand and monitor the activities occurring in the environments.
For example, a surveillance system may be installed in a building to monitor the presence of intruders in the building. In another example, a surveillance system may be installed to monitor the activities of a swimming pool. The real-time images or videos of the swimming pool provided by the surveillance system may be monitored by a person who, upon detecting any possible drowning or accidents in the pool, can send a lifeguard to the pool to assist any swimmers in distress.
Automated surveillance systems are developed to automatically understand and monitor activities in an environment. Therefore, it is not necessary for a person to analyze the captured images or videos in order to understand and monitor the activities in the environment.
In such an automated surveillance system, images or videos of the environment captured by the cameras are processed by a processing unit, for example in a computer, for detecting desired objects in the environment. The detection of desired objects, which are known as foreground objects, is performed using the background subtraction method [2].
In the background subtraction method, a representation of the background scene of the environment is built, and current images of the environment are subsequently compared with the representation of the background scene to determine the foreground objects. Depending on the application of the automated surveillance system, the detected foreground objects may be tracked using a tracking algorithm to monitor the activities of the foreground objects.
To form a representation of the background scene of an environment, each pixel of the background scene is represented by a pixel value, such as luminance or chrominance, and the distribution of the pixel value over time. The distribution of the pixel values over time is normally represented using a probability distribution such as a Normal distribution or a Gaussian distribution. All the distributions of all the pixel values of the background scene form the representation of the background scene (background models) of the environment [1]-[7].
After the representation of the background scene is formed, current images are compared with the representation of the background scene, and the results of the comparison are thresholded to obtain the foreground objects.
The existing automatic surveillance systems using the above-described background subtraction method is more suitable for detecting foreground objects in an environment having a static or a slow-changing background. However, when the background of the environment changes drastically or contains large dynamic noise portion, for example due to reflection of light from a swimming pool, the existing automatic surveillance systems become ineffective in detecting the foreground objects.
Therefore, it is desirable to provide a method for detecting foreground objects effectively in an environment even when the background of the environment changes drastically or contains large dynamic noise portion.